search for: experiments

Hi, suppose I have data from 3 experiments which show conversion as a function of time for different boundary conditions, e.g. pressure, temperature. I want to plot all experiments as conversion over time grouped according to the temperature. However, since I have more than one experiment performed at the same temperature (but different...

I need to run the same worker''s method twice per day with different arguments. Unfortunately, only the second entry in the schedule is firing. I created an experimental worker to verify this: Worker: class ExperimentWorker < BackgrounDRb::MetaWorker set_worker_name :experiment_worker def create(args = nil) # this method is called, when worker is loaded for the first time

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

Como usas la función image puedes consultar la ayuda ?image o help(image) y encontrarás el siguiente ejemplo donde se usa un diferente color Palette (mencionada por pepeceb en su respuesta). x <- 10*(1:nrow(volcano)) y <- 10*(1:ncol(volcano)) image(x, y, volcano, col = terrain.colors(100), axes = FALSE) # O puedes usar directamente el número para indicar el color image(x, y, volcano, col =

Hello Nabble users, A question about having two y axes on a chart. I'm trying to show on the left (first) y axis a measure used in an experiment; the x axis shows the experiment number, that has been sorted in a specific manner -- i.e. not Experiments 1,2,3,n, but could be Experiment 6,1,18,n; the right (second) y axis shows an experimental variable. The idea of the chart is to show how results of the different experiments compare with one of the key variables set in the experiment. The bar chart uses sorted data for its x axis, which have bee...

...ed=y~WSf*WSM, random=list(experiment=pdBlocked(list(pdIdent(~randf-1),pdIdent(~randm-1)))), family=binomial, data=sala.data). Where data neame=sala.glmm which contain y is response wsf is fixed effect wsm is fixed effects randf is random effect random is random effect The data contain three experiments at the same time. The previous cod is work but it does not give me accurate result especially for the random effects. For experiment I wrote this code experiment <- factor(c(rep(1,120),rep(2,120),rep(3,120))) because I have three experiments at the same time, but if I change the experiment t...

I need to use R to model a large number of experiments (say, 1000). Each experiment involves the random selection of 5 numbers (without replacement) from a pool of numbers ranging between 1 and 30. What I need to know is what *proportion* of those experiments contains two or more numbers that are consecutive. So, for instance, an experiment that yield...

Por favor ver comentario con las las letras mayúsculas data("CPS1985", package = "AER") cps <- CPS1985 cps [1:10,] dim( cps) names( cps) library("quantreg") #regresion usual cps_lm <- lm(log(wage) ~ experience + I(experience^2) + + education, data = cps) #regresion por quantiles, tau son los cuantiles

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into consideration the confounding effect? Any help would be greatly appreciated. Isaac A representation of the experiments follows: Experiment 1...

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...I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <- predict(throughput.fit,experiments,type="response") Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor 'No_databases' has new level(s) 200, 400, 600, 800, 1000 Of course these are new factor levels, it is exactly what I am trying to achieve i.e. extrapolate the v...

...to specify a correct random formula for a mixed model, that specifies no random effect on a higher level? I have the following dataset of timeseries of respiration measurements (column resp) of biomass including a parameter initial biomass x0. Respiration measurements have been recorded in several experiments, each consisting of several replicates. str(rd) 'data.frame': 3229 obs. of 5 variables: $ suite : Factor w/ 3 levels "Face","Fal","Pushchino": 1 1 1 1 1 1 1 1 1 1 ... $ experiment: Factor w/ 44 levels "1","2","3","4&...

Hi all- Over the past week or two, I've been trying out asterisk under Fedora 1 Linux (RedHat). In my setup (single and dual Xeon motherboards), I have so far had a very good experience in terms of performance. In doing E1 load testing, I've found that Fedora handles heavy load much better than RedHat9, probably because of its better use of the multi-threading capabilities of the Xeon.

Hello, I'd like to help improve the debug experience in LLVM/Clang when optimizations are enabled, but I'm not sure where to start. I've seen the umbrella bug for poor debug experiences (38768 https://bugs.llvm.org/show_bug.cgi?id=38768 ). Is there any bug in this list that isn't being looked at (and that doesn't require a lot of knowledge) that I could work on? I also tried

...ed=y~WSf*WSM, random=list(experiment=pdBlocked(list(pdIdent(~randf-1),pdIdent(~randm-1)))), family=binomial, data=sala.data). Where data neame=sala.glmm which contain y is response wsf is fixed effect wsm is fixed effects randf is random effect random is random effect The data contain three experiments at the same time. The previous cod is work but it does not give me accurate result especially for the random effects. For experiment I wrote this code experiment <- factor(c(rep(1,120),rep(2,120),rep(3,120))) because I have three experiments at the same time, but if I change the experiment t...

> CAN YOU TELL ME HOW TO FIT FIXED-EFFECTS MODEL WITH R? THANK YOU! Ordinary lm() might suffice. In the code below, I try to simulate a dataset from a standard earnings regression, where log earnings is quadratic in experience, but the intercept floats by education category - you have 4 intercepts for 4 education categories. I think this works as a simple implementation of "the fixed

Hi, I am running a learning experiment in which both training subjects and controls complete a pretest and posttest. All analyses are being conducted in R. We are looking to compare two training methodologies, and so have run this experiment twice, once with each methodology. Methodology is a between-subjects factor. Trying to run this analysis with every factor included (ie, subject as a

...time, I would like to know how to filter down the (2 x 1000) matrix to contain only those elements with a particular B-value so that I can then calculate the conditional means of the A-values. -- View this message in context: http://www.nabble.com/Calculating-conditional-mean-of-large-series-of-experiments-tp17536218p17536218.html Sent from the R help mailing list archive at Nabble.com.

Hi All, I'm pretty new to this list and to CentOS, so forgive me if this kind of thing is shunned. I have really enjoyed following some threads on this list and learning what everyone out there is doing with the OS. I actually got started on CentOS when researching how to study for the RHCE without forking out big cash for Red Hat licenses, a donation is coming soon. Anyway, I'm

Hi everyone, I'm happy to report that I have two open postdoctoral research positions at Argonne National Laboratory focusing on LLVM-based compiler technology for high-performance computing. US citizenship is *not* required. If you've received your Ph.D. within the past several years, or are in the process of completing your degree requirements, and would like to join a team working